Algorithm Design for a Cytokine Release Assay of Antigen-Specific in Vitro Stimuli of Circulating Leukocytes to Classify Leprosy Patients and Household Contacts

• Type of Material: Article
• Authors: Pedro Henrique Ferreira Marçal, Universidade Vale do Rio Doce; Márcio Luís Moreira De Souza, Universidade Federal de Juiz de Fora; Rafael Silva Gama, Universidade Vale do Rio Doce; Lorena Bruna De Oliveira, Universidade Vale do Rio Doce; Matheus de Souza Gomes, Universidade Federal de Uberlândia; Laurence Rodrigues Do Amaral, Universidade Federal de Uberlândia; Roberta Olmo Pinheiro, Fundacao Oswaldo Cruz; Euzenir Nunes Sarno, Fundacao Oswaldo Cruz; Milton Ozório Moraes, Fundacao Oswaldo Cruz; Jessica Fairley, Emory University; Olindo Assis Martins-Filho, Fundacao Oswaldo Cruz; Lucia Alves De Oliveira Fraga, Universidade Federal de Juiz de Fora
• Language: English
• Date: 2022-03-01
• Publisher: Oxford Academic
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/ofid/ofac036
• Title of Journal or Parent Work: Open Forum Infectious Diseases
• Volume: 9
• Issue: 3
• Start Page: ofac036
• End Page: ofac036
• Grant/Funding Information: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.; O. A. M.-F. has received Productivity in Research fellowships from CNPq and is a research fellow for Fundação de Amparo à Pesquisa do Estado do Amazonas (PVN-II, PRÓ-ESTADO program number 005/2019).; Fundação de Amparo à Pesquisa de Minas Gerais (CBB-APQ-01379-15); Fundação Nacional de Saúde – Ministério da Saúde, Brazil (TC 304/2013/FNS/MS);; This work was supported by Conselho de Desenvolvimento Tecnológico e Científico, Brazilian National Council for Scientific and Technological Development (CNPq) (DECIT 2008, DECIT 2012 and CNPQ/MS/NIH 404189/2019-9)
• Keywords: differential diagnosis; cytokine assay; leprosy; household contacts; algorithm design
• Research Categories: Health Sciences, Public Health; Biology, Bioinformatics; Health Sciences, Epidemiology; Biology, Molecular

Abstract

Background Immunological biomarkers have often been used as a complementary approach to support clinical diagnosis in several infectious diseases. The lack of commercially available laboratory tests for conclusive early diagnosis of leprosy has motivated the search for novel methods for accurate diagnosis. In the present study, we describe an integrated analysis of a cytokine release assay using a machine learning approach to create a decision tree algorithm. This algorithm was used to classify leprosy clinical forms and monitor household contacts. Methods A model of Mycobacterium leprae antigen-specific in vitro assay with subsequent cytokine measurements by enzyme-linked immunosorbent assay was employed to measure the levels of tumor necrosis factor (TNF), interferon-γ, interleukin 4, and interleukin 10 (IL-10) in culture supernatants of peripheral blood mononuclear cells from patients with leprosy, healthy controls, and household contacts. Receiver operating characteristic curve analysis was carried out to define each cytokine’s global accuracy and performance indices to identify clinical subgroups. Results Data demonstrated that TNF (control culture [CC]: AUC = 0.72; antigen-stimulated culture [Ml]: AUC = 0.80) and IL-10 (CC: AUC = 0.77; Ml: AUC = 0.71) were the most accurate biomarkers to classify subgroups of household contacts and patients with leprosy, respectively. Decision tree classifier algorithms for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC: 79% [48/61]; Ml: 84% [51/61]). Additionally, IL-10 analysis categorized leprosy patients’ subgroups with moderate accuracy (CC: 73% [22/30] and Ml: 70% [21/30]). Conclusions Together, our findings demonstrated that a cytokine release assay is a promising method to complement clinical diagnosis, ultimately contributing to effective control of the disease.